The function-as-a-service (FaaS) paradigm is envisioned as the next generation of cloud computing systems that mitigate the burden for cloud-native application developers by abstracting them from cloud resource management. However, it does not deal with the application data aspects. As such, developers have to intervene and undergo the burden of managing the application data, often via separate cloud storage services. To further streamline cloud-native application development, in this work, we propose a new paradigm, known as Object as a Service (OaaS) that encapsulates application data and functions into the cloud object abstraction. OaaS relieves developers from resource and data management burden while offering built-in optimization features. Inspired by OOP, OaaS incorporates access modifiers and inheritance into the serverless paradigm that: (a) prevents developers from compromising the system via accidentally accessing underlying data; and (b) enables software reuse in cloud-native application development. Furthermore, OaaS natively supports dataflow semantics. It enables developers to define function workflows while transparently handling data navigation, synchronization, and parallelism issues. To establish the OaaS paradigm, we develop a platform namedOparacathat offers state abstraction for structured and unstructured data with consistency and fault-tolerant guarantees. We evaluated Oparaca under real-world settings against state-of-the-art platforms with respect to the imposed overhead, scalability, and ease of use. The results demonstrate that the object abstraction provided by OaaS can streamline flexible and scalable cloud-native application development with an insignificant overhead on the underlying serverless system.

ABSTRACT Background Confidential computing has gained prominence due to the escalating volume of data‐driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly across distributed environments, such as the edge‐to‐cloud continuum. Objective Provided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts and cutting‐edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. Methods We underscore the significance of building trust at both the hardware and software levels and delve into their applications, particularly for regular and advanced machine learning (ML) (e.g., large language models (LLMs), computer vision) applications. Results While substantial progress has been made, there are some barely‐explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and address vulnerabilities of the existing trusted execution environments. Conclusion Providing a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers.

Service liquidity across edge-to-cloud or multi-cloud will serve as the cornerstone of the next generation of cloud computing systems (Cloud 2.0). Provided that cloud-based services are predominantly containerized, an efficient and robust live container migration solution is required to accomplish service liquidity. In a nod to this growing requirement, in this research, we leverage FastFreeze, a popular platform for process check-point/restore within a container, and promote it to be a robust solution for end-to-end live migration of containerized services. In particular, we develop a new platform, called FastMig that proactively controls the checkpoint/restore operations of FastFreeze, thereby, allowing for robust live migration of containerized services via standard HTTP interfaces. The proposed platform introduces post-checkpointing and pre-restoration operations to enhance migration robustness. Notably, the pre-restoration operation includes containerized service startup options, enabling warm restoration and reducing the migration downtime. In addition, we develop a method to make FastFreeze robust against failures that commonly happen during the migration and even during the normal operation of a containerized service. Experimental results under real-world settings show that the migration downtime of a containerized service can be reduced by 30X compared to the situation where the original FastFreeze was deployed for the migration. Moreover, we demonstrate that FastMig and warm restoration method together can significantly mitigate the container startup overhead. Importantly, these improvements are achieved without any significant performance reduction and only incurs a small resource usage overhead, compared to the bare (i.e., non-FastFreeze) containerized services.

Current Serverless abstractions (e.g., FaaS) poorly support non-functional requirements (e.g., QoS and constraints), are provider-dependent, and are incompatible with other cloud abstractions (e.g., databases). As a result, application developers have to undergo numerous rounds of development and manual deployment refinements to finally achieve their desired quality and efficiency. In this paper, we present Object-as-a-Service (OaaS)---a novel serverless paradigm that borrows the object-oriented programming concepts to encapsulate business logic, data, and non-functional requirements into a single deployment package, thereby streamlining provider-agnostic cloud-native application development. We also propose a declarative interface for the non-functional requirements of applications that relieves developers from daunting refinements to meet their desired QoS and deployment constraint targets. We realized the OaaS paradigm through a platform called Oparaca and evaluated it against various real-world applications and scenarios. The evaluation results demonstrate that Oparaca can enhance application performance by 60× and improve reliability by 50× through latency, throughput, and availability enforcement---all with remarkably less development and deployment time and effort.

Summary Along with the rise of domain‐specific computing (ASICs hardware) and domain‐specific programming languages, we envision that the next step is the emergence of domain‐specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop serverless multimedia streaming engine (SMSE), the first domain‐specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end‐users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users' quality of service. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data‐dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real‐world settings demonstrates its capability to reduce both the containerization overhead, and the makespan time of serving multimedia processing functions (by up to 30%) in compare to the function provision methods that are being used in the general‐purpose serverless cloud systems.

Serverless edge systems simplify the deployment of real-time AI-based Internet of Things (IoT) applications at the edge. However, the heterogeneity of edge computing nodes – in terms of both hardware and software – makes load balancing challenging in these systems. In this paper, we propose a performance-driven, empirical weight-tuning approach to achieve effective load balancing based on the characteristics and capabilities of the nodes. By extensively profiling the nodes, we gather knowledge on performance metrics such as throughput, energy efficiency, response time, AI accuracy, and cost. Using this acquired knowledge, we introduce a weighted round-robin strategy to optimize the performance metrics according to their observed significance. To address multiple objectives, we introduce a multi-objective method that aims to strike a balance between any arbitrary set of performance objectives simultaneously. Additionally, we explore a coordinated distributed approach to overcome the limitations of centralized load balancing. Next, we introduce Hedgi, a heterogeneous serverless edge architecture designed to efficiently configure and utilize the derived load balancing policies, validated empirically. To demonstrate the practicality of Hedgi, we containerize and serverlessize a real-time object detection application. Extensive empirical studies are conducted using Hedgi to evaluate the performance of the proposed load balancing approach. The results provide valuable insights into the design trade-offs of various load balancing policies and system designs in the heterogeneous serverless edge.

Function as a Service (FaaS) paradigm is becoming widespread and is envisioned as the next generation of cloud computing systems that mitigate the burden for programmers and cloud solution architects. However, the FaaS abstraction only makes the cloud resource management aspects transparent but does not deal with the application data aspects. As such, developers have to intervene and undergo the burden of managing the application data, often via separate cloud services (e.g., AWS S3). Similarly, the FaaS abstraction does not natively support function workflow, hence, the developers often have to work with workflow orchestration services (e.g., AWS Step Functions) to build workflows. Moreover, they have to explicitly navigate the data throughout the workflow. To overcome these inherent problems of FaaS, our hypothesis is to design a higher-level cloud programming abstraction that can hide the complexities and mitigate the burden of developing cloud-native application development. Accordingly, in this research, we borrow the notion of object from object-oriented programming and propose a new abstraction level atop the function abstraction, known as Object as a Service (OaaS). OaaS encapsulates the application data and function into the object abstraction and relieves the developers from resource and data management burdens. It also unlocks opportunities for built-in optimization features, such as software reusability, data locality, and caching. OaaS natively supports dataflow programming such that developers define a workflow of functions transparently without getting involved in data navigation, synchronization, and parallelism aspects. We implemented a prototype of the OaaS platform and evaluated it under real-world settings against state-of-the-art platforms regarding the imposed overhead, scalability, and ease of use. The results demonstrate that OaaS streamlines cloud programming and offers scalability with an insignificant overhead to the underlying cloud system.

Containerized services deployed within various computing systems, such as edge and cloud, desire live migration support to enable user mobility, elasticity, and load balancing. To enable such a ubiquitous and efficient service migration, a live migration solution needs to handle circumstances where users have various authority levels (full control, limited control, or no control) over the underlying computing systems. Supporting the live migration at these levels serves as the cornerstone of interoperability, and can unlock several use cases across various forms of distributed systems. As such, in this study, we develop a ubiquitous migration solution (called UMS) that, for a given containerized service, can automatically identify the feasible migration approach, and then seamlessly perform the migration across autonomous computing systems. UMS does not interfere with the way the orchestrator handles containers and can coordinate the migration without the orchestrator involvement. Moreover, UMS is orchestrator-agnostic, i.e., it can be plugged into any underlying orchestrator platform. UMS is equipped with novel methods that can coordinate and perform the live migration at the orchestrator, container, and service levels. Experimental results show that for single-process containers, the service-level approach, and for multi-process containers with small$(< 128 \mathbf{MiB})$memory footprint, the container-level migration approach lead to the lowest migration overhead and service downtime. To demonstrate the potential of UMS in realizing interoperability and multi-cloud scenarios, we examined it to perform live service migration across heterogeneous orchestrators, and between Microsoft Azure and Google Cloud.

Summary Serverless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN)-the centerpiece of DL applications-practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.

Summary Cloud‐based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses dealing with confidential big data (e.g., criminal reports) are reluctant to fully embrace such cloud services due to valid data privacy concerns. Solutions based on client‐side encryption have been developed to mitigate these concerns. Nonetheless, such solutions hinder data processing, especially, data clustering, which is pivotal in applications such as real‐time search on large corpora (e.g., big datasets). To cluster encrypted big data, we propose privacy‐preserving clustering schemes, called ClusPr, for three forms of unstructured datasets, namely static, semi‐dynamic, and dynamic. ClusPr functions based on statistical characteristics of the datasets to: (A) determine the suitable number of clusters; (B) populate the clusters with topically relevant tokens; and (C) adapt the cluster set based on the dynamism of the underlying dataset. Experimental results, obtained from evaluating ClusPr against other schemes in the literature, on three different test datasets demonstrate between and improvement on the cluster coherency. Moreover, we notice that employing ClusPr within a privacy‐preserving enterprise search system can reduce the search time by up to , while improving the search accuracy by up to .

Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests (i.e., tasks). We propose a reusing paradigm for the tasks that are waiting for execution. This paradigm can be particularly impactful in serverless platforms where multiple users can request similar services simultaneously. Our motivation is a multimedia streaming engine that processes the media segments in an on-demand manner. We propose a mechanism to identify various types of “mergeable” tasks and aggregate them to improve the QoS and mitigate the incurred cost. We develop novel approaches to determine when and how to perform task aggregation such that the QoS of other tasks is not affected. Evaluation results show that the proposed mechanism can improve the QoS by significantly reducing the percentage of tasks missing their deadlines and reduce the overall time (and subsequently the incurred cost) of utilizing cloud services by more than 9 percent.

Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness at Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average ≈24% for plain-text and ≈75% for encrypted generic datasets.

Cloud providers offer a variety of execution platforms in form of bare-metal, VM, and containers. However, due to the pros and cons of each execution platform, choosing the appropriate platform for a specific cloud-based application has become a challenge for solution architects. The possibility to combine these platforms (e.g., deploying containers within VMs) offers new capacities that makes the challenge even further complicated. However, there is a little study in the literature on the pros and cons of deploying different application types on various execution platforms. In particular, evaluation of diverse hardware configurations and different CPU provisioning methods, such as CPU pinning, have not been sufficiently studied in the literature. In this work, the performance overhead of container, VM, and bare-metal execution platforms are measured and analyzed for four categories of real-world applications, namely video processing, parallel processing (MPI), web processing, and No-SQL, respectively representing CPU intensive, parallel processing, and two IO intensive processes. Our analyses reveal a set of interesting and sometimes counterintuitive findings that can be used as best practices by the solution architects to efficiently deploy cloud-based applications. Here are some notable mentions: (A) Under specific circumstances, containers can impose a higher overhead than VMs; (B) Containers on top of VMs can mitigate the overhead of VMs for certain applications; (C) Containers with a large number of cores impose a lower overhead than those with a few cores.

Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at a Base Station, it is challenging for the V2I system to maintain its performance, which can lead to hazardous consequences. Hence, the goal of this research is to develop a V2I system that is robust against uncertain request arrivals. To achieve this goal, we propose to dynamically allocate service requests among Base Stations. We develop an uncertainty-aware resource allocation method for the federated environment that assigns arriving requests to a Base Station so that the likelihood of completing it on-time is maximized. We evaluate the system under various workload conditions and oversubscription levels. Simulation results show that edge federation can improve robustness of the V2I system by reducing the overall service miss rate by up to 45%.

Video streaming is growing in popularity and has become the most bandwidth-consuming Internet service. As such, robust streaming in terms of low latency and uninterrupted streaming experience, particularly for viewers in distant areas, has become a challenge. The common practice to reduce latency is to pre-process multiple versions of each video and use Content Delivery Networks (CDN) to cache videos that are popular in a geographical area. However, with the fast-growing video repository sizes, caching video contents in multiple versions on each CDN is becoming inefficient. Accordingly, in this paper, we propose the architecture for Fog Delivery Networks (FDN) and provide methods to federate them (called F-FDN) to reduce video streaming latency. In addition to caching, FDNs have the ability to process videos in an on-demand manner. F-FDN leverages cached contents on the neighboring FDNs to further reduce latency. In particular, F-FDN is equipped with methods that aim at reducing latency through probabilistically evaluating the cost benefit of fetching video segments either from neighboring FDNs or by processing them. Experimental results against alternative streaming methods show that both on-demand processing and leveraging cached video segments on neighboring FDNs can remarkably reduce streaming latency (on average 52%).

In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (e.g., those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task's probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping and we find thresholds for dropping and deferring. Next, we develop a pruning-aware mapping heuristic and extend it to engender fairness across various task types. We show the cost benefit of using probabilistic pruning in an HC system. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by ≃25%) and cost in an oversubscribed HC system by up to ≃40%.

Video streaming providers generally have to store several formats of the same video and stream the appropriate format based on the characteristics of the viewer’s device. This approach, called pre-transcoding, incurs a significant cost to the stream providers that rely on cloud services. Furthermore, pre-transcoding proven to be inefficient due to the long-tail access pattern to video streams. To reduce the incurred cost, we propose to pre-transcode only frequently accessed videos (called hot videos) and partially pre-transcode others, depending on their hotness degree. Therefore, we need to measure video stream hotness. Accordingly, we first provide a model to measure the hotness of video streams. Then, we develop methods that operate based on the hotness measure and determine how to pre-transcode videos to minimize the cost of stream providers. The partial pre-transcoding methods operate at different granularity levels to capture different patterns in accessing videos. Particularly, one of the methods operates faster but cannot partially pre-transcode videos with the non-long-tail access pattern. Experimental results show the efficacy of our proposed methods, specifically, when a video stream repository includes a high percentage of the Frequently Accessed Video Streams and a high percentage of videos with the non-long-tail accesses pattern.

Summary Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, the potential of cloud services has not been fully realized due to users concerns about data privacy and security. User‐side encryption techniques can be employed to mitigate the security concerns, but once the data is encrypted, no processing (eg, searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are encrypted. These techniques enable various types of search on the encrypted data and offer different levels of security. While these techniques enable different search types and vary in details, they share similarities in their components and architectures. In this paper, we provide a comprehensive survey on different secure search techniques, a high‐level architecture for these systems, and an analysis of their performance and security level.

Summary Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client‐end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real‐time searching over the encrypted big data in the cloud. In particular, semantic search is of interest to clients dealing with big data. To address this problem, in this research, we develop a system (termed S3BD) for searching big data using cloud services without exposing any data to cloud providers. To keep real‐time response on big data, S3BD proactively prunes the search space to a subset of the whole dataset. For that purpose, we propose a method to cluster the encrypted data. An abstract of each cluster is maintained on the client‐end to navigate the search operation to appropriate clusters at the search time. Results of experiments, carried out on real‐world big datasets, demonstrate that the search operation can be achieved in real‐time and is significantly more efficient than other counterparts. In addition, a fully functional prototype of S3BD is made publicly available.

High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.

Ultra reliable, low latency vehicle-to- infrastructure (V2I) communications is a key requirement for seamless operation of autonomous vehicles (AVs) in future smart cities. To this end, cellular small base stations (SBSs) with edge computing capabilities can reduce the end-to-end (E2E) service delay by processing requested tasks from AVs locally, without forwarding the tasks to a remote cloud server. Nonetheless, due to the limited computational capabilities of the SBSs, coupled with the scarcity of the wireless bandwidth resources, minimizing the E2E latency for AVs and achieving a reliable V2I network is challenging. In this paper, a novel algorithm is proposed to jointly optimize AVs-to-SBSs association and bandwidth allocation to maximize the reliability of the V2I network. By using tools from labor matching markets, the proposed framework can effectively perform distributed association of AVs to SBSs, while accounting for the latency needs of AVs as well as the limited computational and bandwidth resources of SBSs. Moreover, the convergence of the proposed algorithm to a core allocation between AVs and SBSs is proved and its ability to capture interdependent computational and transmission latencies for AVs in a V2I network is characterized. Simulation results show that by optimizing the E2E latency, the proposed algorithm substantially outperforms conventional cell association schemes, in terms of service reliability and latency.

Video streams, either in the form of Video On-Demand (VOD) or live streaming, usually have to be converted (i.e., transcoded) to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Transcoding is a computationally expensive and time-consuming operation. Therefore, streaming service providers have to store numerous transcoded versions of a given video to serve various display devices. With the sharp increase in video streaming, however, this approach is becoming cost-prohibitive. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand (i.e., lazy) manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding, however, is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, in this paper, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling component that maps transcoding tasks to the Virtual Machines (VMs) by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner component. The component provides a self-configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Simulation results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider by up to 85 percent.

Summary Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud‐based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User‐side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user‐transparent and Cloud‐agnostic regular‐expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular‐expression search functionality over encrypted data by translating the search queries on‐the‐fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large‐scale (and potentially big data scale) data‐sets. We evaluate the performance of RESeED experimentally and demonstrate its scalability and correctness using real‐world data‐sets fromarXiv.organd Internet Engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (≃6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with thegreputility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd.

The recent rapid growth in Internet speeds and file storage requirements has made cloud storage an appealing option on both a personal and enterprise level. Despite the many benefits offered by cloud storage, many potential users with sensitive data refrain from fully utilizing this service due to valid concerns about information privacy. An established solution to this concern is to perform encryption on the user side with the key stored on a local machine, meaning the cloud will never see the user's plaintext data. However, by encrypting data on the user side data processing capabilities (e.g., searching) are lost. In particular, the ability to semantically search is of the user's interest in large datasets. In this paper, we present S3C, a system that provides a semantic search functionality over encrypted data in the cloud. S3C combines approaches from traditional keyword-based searchable encryption and semantic web searching. It offers a user transparent experience that accepts a simple multi-phrase query and returns a list of documents ranked by semantic relevance to the query. Our proposed approach is space-efficient, which makes it suitable for large scale datasets. Our minimal processing also allows the system to be run on thin clients such as smart-phones or tablets. We evaluate the performance of our system against various real-world datasets, and our results show that it produces accurate search results while maintaining minimal storage overhead (~0.3% of the dataset size).

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics (e.g., spatial resolution) of clients' devices. Transcoding is a computationally expensive operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. However, recent studies show that accessing video streams have a long tail distribution. That is, there are few popular videos that are frequently accessed while the majority of them are accessed infrequently. The idea we propose in this research is to transcode the infrequently accessed videos in a on-demand (i.e., lazy) manner. Due to the cost of maintaining infrastructure, streaming service providers (e.g., Netflix) are commonly using cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address the challenge, in this research, we present an architecture for on-demand transcoding of video streams. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) requirements of video streams. In particular, the architecture includes a QoS-aware scheduling component to efficiently map video streams to cloud resources, and a cost-efficient dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS requirements.

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics of clients' devices (e.g., spatial resolution, network bandwidth, and supported formats). Transcoding is a computationally expensive and time-consuming operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. Due to the expense of maintaining and upgrading storage and computing infrastructures, many streaming service providers (e.g., Netflix) recently are becoming reliant on cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address this challenge, in this paper, we present the Cloud-based Video Streaming Service (CVSS) architecture to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Clouds are becoming prevalent service providers because of their low upfront costs, rapid application deployment, and high scalability. Many users outsource their sensitive data and services to cloud providers. Users frequently access these sensitive services through devices and connections that are vulnerable to thieving and eavesdropping. Therefore, users are desperate of robust security measures to protect their data and services privacy in clouds. In particular, robust authentication techniques are demanded by users for safe access to cloud services. One technique is to utilize multiple authentication factors (a.k. A multi-factor authentication) to access cloud services. However, the challenge is that the multi-factor authentication technique is not effective as it causes user frustration and fatigue. To address this challenge, in this study, we propose a multi-factor authentication architecture that aims at minimizing the perceived authentication hardship for cloud users while improving the security of the authentication. To achieve the goal, our authentication architecture suggests a progressive manner to leverage access to different levels of cloud services. At each level, the architecture asks for authentication factors by considering the perceived hardship for users. To increase the security and user convenience, the architecture also considers implicit authentication factors in addition to the explicit factors. Our evaluation results indicate that authentication using the proposed architecture decreases the users' perceived hardship up to 29% in compare with other methods. The results also reveal that our proposed architecture adapts the authentication difficulty based on the user condition.

Capabilities for trustworthy cloud-based computing and data storage require usable, secure and efficient solutions which allow clients to remotely store and process their data in the cloud. In this paper, we present RESeED, a tool which provides user-transparent and cloud-agnostic search over encrypted data using regular expressions without requiring cloud providers to make changes to their existing infrastructure. When a client asks RESeED to upload a new file in the cloud, RESeED analyzes the file's content and updates novel data structures accordingly, encrypting and transferring the new data to the cloud. RESeED provides regular expression search over this encrypted data by translating queries on-the-fly to finite automata and analyzes efficient and secure representations of the data before asking the cloud to download the encrypted files. We evaulate a working prototype of RESeED experimentally (currently publicly available) and show the scalability and correctness of our approach using real-world data sets from arXiv.org and the IETF. We show absolute accuracy for RESeED, with very low (6%) overhead, and high performability, even beating grep for some benchmarks.

We present Reseed, a tool that provides user-transparent and Cloud-agnostic regular expression search over encrypted data without requiring trust in the Cloud, or changes to Cloud infrastructure. Upon receiving a search query, Reseed translates it to a finite automata and analyzes efficient and secure representations of the data before asking the Cloud to download the matching encrypted files. We demonstrate and evaluate a working prototype of Reseed and show the scalability and correctness of our approach using data from arXiv.org. For more details see our Technical Report.

SUMMARY Resource provisioning is one of the main challenges in large‐scale distributed systems such as federated Grids. Recently, many resource management systems in these environments have started to use the lease abstraction and virtual machines (VMs) for resource provisioning. In the large‐scale distributed systems, resource providers serve requests from external users along with their own local users. The problem arises when there is not sufficient resources for local users, who have higher priority than external ones, and need resources urgently. This problem could be solved by preempting VM‐based leases from external users and allocating them to the local ones. However, preempting VM‐based leases entails side effects in terms of overhead time as well as increasing makespan of external requests. In this paper, we model the overhead of preempting VMs. Then, to reduce the impact of these side effects, we propose and compare several policies that determine the proper set of lease(s) for preemption. We evaluate the proposed policies through simulation as well as real experimentation in the context of InterGrid under different working conditions. Evaluation results demonstrate that the proposed preemption policies serve up to 72% more local requests without increasing the rejection ratio of external requests. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY The paper describes creation of a contention‐aware environment in a large‐scale distributed system where the contention occurs to access resources between external and local requests. To resolve the contention, we propose and implement a preemption mechanism in the InterGrid platform, which is a platform for large‐scale distributed system and uses virtual machines for resource provisioning. The implemented mechanism enables the resource providers to increase their resource utilization through contributing resources to the InterGrid platform without delaying their local users. The paper also evaluates the impact of applying various policies for preempting user requests. These policies affect resource contention, average waiting time, and imposed overhead to the system. Experiments conducted in real settings demonstrate efficacy of the preemption mechanism in resolving resource contention and the influence of preemption policies on the amount of imposed overhead and average waiting time. Copyright © 2013 John Wiley & Sons, Ltd.

Energy efficiency is one of the main challenge hat data centers are facing nowadays. A considerable portion of the consumed energy in these environments is wasted because of idling resources. To avoid wastage, offering services with variety of SLAs (with different prices and priorities) is a common practice. The question we investigate in this research is how the energy consumption of a data center that offers various SLAs can be reduced. To answer this question we propose an adaptive energy management policy that employs virtual machine(VM) preemption to adjust the energy consumption based on user performance requirements. We have implementedour proposed energy management policy in Haize a as a real scheduling platform for virtualized data centers. Experimental results reveal 18% energy conservation (up to 4000 kWh in 30 days) comparing with other baseline policies without any major increase in SLA violation.

Many applications in federated Grids have quality-of-service (QoS) constraints such as deadline. Admission control mechanisms assure QoS constraints of the applications by limiting the number of user requests accepted by a resource provider. However, in order to maximize their profit, resource owners are interested in accepting as many requests as possible. In these circumstances, the question that arises is: what is the effective number of requests that can be accepted by a resource provider in a way that the number of accepted external requests is maximized and, at the same time, QoS violations are minimized. In this paper, we answer this question in the context of a virtualized federated Grid environment, where each Grid serves requests from external users along with its local users and requests of local users have preemptive priority over external requests. We apply analytical queuing model to address this question. Additionally, we derive a preemption-aware admission control policy based on the proposed model. Simulation results under realistic working conditions indicate that the proposed policy improves the number of completed external requests (up to 25%). In terms of QoS violations, the 95% confidence interval of the average difference with other policies is between (14.79%, 18.56%).