Rising computational demands have made cloud datacenters' water footprint a critical concern. We demonstrate how different water footprint accounting methodologies - incorporating operational, manufacturing, and decommissioning water consumption, impact measurements and highlight the need for methodology standardization for water-aware operations. Our analysis reveals opportunities for water-aware scheduling in datacenters by considering regional water variations and lifecycle impacts.

We present GridGreen, a scheduling framework that improves the sustainability and performance of scientific workflow execution by integrating serverless computing with traditional on-premise high performance computing (HPC) clusters. GridGreen allocates workflow components across HPC and serverless environments leveraging spatio-temporal variation of carbon intensity and component execution characteristics. It incorporates component-level optimization, speculative pre-warming, I/O-aware data management, and fallback adaptation to jointly minimize carbon footprint and service time under user-defined cost constraints. Our evaluations on large-scale bioinformatics workflows across leadership-class HPC facilities and cloud-based serverless regions demonstrate that GridGreen achieves robust, cost-effective execution while improving carbon efficiency.

Although serverless platforms have been extremely popular recently, certain kinds of applications are not well-supported by these platforms. For example, they work well for workloads that transform or aggregate bulk data but not for applications where predictable response times are crucial. These workloads have fine-grained tasks and stringent service level agreements (SLAs). Current serverless platforms’ overheads and resource contention lead to variable performance, making using them for real-time online applications impossible. We demonstrate Anubis, a new platform built on top of OpenWhisk, that helps meet SLAs for response-time-sensitive serverless workloads, even when multiple concurrent workloads compete for various resource types. Anubis’s approach centers on multi-resource fair queuing. Even as stateless functions compete for CPU, storage, and network resources, Anubis ensures each gets its fair share according to its dominant resource needs. We show that when various serverless workloads are intermixed, Anubis’s approach can reduce SLA violations by 15–34%, improving max-min fairness by 2× compared to competing scheduling policies.

We propose Nimble, a QoS-aware resource management framework for edge-assisted microservice environments. We build a preliminary prototype of Nimble and show its applicability in terms of resource utilization and execution latency for microservices in a small-scale testbed setup.

The recent expansion of mobile IoT devices (MIoTDs) along with the exposure of many compute-intensive and latency-critical applications, have given a step rise to the mobile edge computing (MEC) platform to process computational microservices at the edge. The paramount importance of designing an effective incentive mechanism is a very important topic for such systems to get a fair amount of resources and provide incentives to MIoDs. Hence, we design a MEC platform with heterogeneous MIoTDs participating in a computational microservice offloading scheme. Here, we propose an incentive approach applying a double auction mechanism to incentivize the involvement of MIoTDs. In practice, the incentive mechanism typically interacts with the demand estimation scheme that estimates the demand profile of MIoTDs. As a result, we design a novel mechanism for microservices –microservice Incentive Service Offloading (mISO), which comprises an incentive approach and a demand estimation scheme. The mISO mechanism holds truthfulness, rationality, and low computational complexity while guaranteeing positive social welfare and generating the optimal demand profiles for MIoTDs. Simulation results showed that mISO provides 18–21$\%$and 25–30$\%$improvements in terms of average latency and resource utilization compared to existing works.

Due to ubiquitous increase of mobile services and powerful Internet-of-Things (IoT) devices, the interest for mobile-edge computing (MEC) solutions has grown both in industry and academia. One of the fundamental mechanisms of MEC is offloading, i.e., delegation of a computation from the user to a server (set) placed near to the edge. Edge servers may have poor incentives to run a delegated service, for example, for the temporary limited resources. In this article, we dissect the incentive mechanisms within a MEC ecosystem with the aim of ensuring a fault-tolerant edge service under unreliable scenarios. In particular, we design an auction mechanism to model the interaction between the MEC players, and model the edge users’ probability of successful offloading, assuming that the cost of executing each offloading request is private. Scrutinizing the demand uncertainty of edge users, the main motive of our auction method is to optimize the offloading cost to engage more edge users in this process, while imposing probabilistic guarantees of offloading service execution. Our offloading cost minimization problem is considered to be an NP-hard. For the solution, we use a heuristic methodology to get the optimal approximation ratio and provide economical fairness. We provide exhaustive simulation results to show the excellent performance of our scheme.

Cloud-Assisted Wireless Body Area Networks (WBANs) is one of the key emerging technology for medical applications in the era of Internet-of-Things. In cloud-assisted WBANs, the WBANs are able to access the cloud resources, which are geographically close to the local Access Points (APs). However, in the presence of poor radio link-quality between cloud-assisted WBANs and APs, energy consumption and service delay of the network dynamically changed. Therefore, in order to solve this problem, we propose an energy-efficient Body-to-Body (B2B) communications among coexisting WBANs to relay the information of the patients to nearest APs to provide real-time healthcare services. On the other hand, the healthcare services enabled for cloud-assisted WBANs is popularly known as Healthcare-as-a-Service (He-aaS). In He-aaS, it is very challenging to finalize a price agreement between WBANs and Health-Cloud Service Providers (H-CSPs) as WBANs follow a heterogeneous architecture. The existing pricing mechanisms, which are constrained to the homogeneous applications, are not compatible to He-aaS. We propose an optimal and distributed pricing policy for He-aaS in order to increase the profit level of each WBAN with the consideration of the optimal QoS and energy. Analysis of the proposed algorithms and the inferences of the results validates the usefulness of the proposed energy-efficient B2B communication and distributed pricing policy. Extensive results indicate that our proposed schemes are able to efficiently utilize limited resource with optimal QoS and energy consideration.

In recent years, the rapid development of mobile edge computing (MEC) provides an efficient execution platform at the edge for Internet-of-Things (IoT) applications. Nevertheless, the MEC also provides optimal resources to different microservices, however, underlying network conditions and infrastructures inherently affect the execution process in MEC. Therefore, in the presence of varying network conditions, it is necessary to optimally execute the available task of end users while maximizing the energy efficiency in edge platform and we also need to provide fair Quality-of-Service (QoS). On the other hand, it is necessary to schedule the microservices dynamically to minimize the total network delay and network price. Thus, in this article, unlike most of the existing works, we propose a dynamic microservice scheduling scheme for MEC. We design the microservice scheduling framework mathematically and also discuss the computational complexity of the scheduling algorithm. Extensive simulation results show that the microservice scheduling framework significantly improves the performance metrics in terms of total network delay, average price, satisfaction level, energy consumption rate (ECR), failure rate, and network throughput over other existing baselines.

The microservice architecture provides high agility, making it a suitable choice for implementing edge cloud services. Provisioning microservices at the network edge requires the dynamic allocation of resources. However, due to the resource limitation in the edge cloud environment, there is no guarantee that enough resources are always available upon a microservice's requests. In this paper, we design an online auction-based mechanism to incentivize microservices to spare their occupied resources so that the edge cloud platform can reclaim them and reallocate them to other microservices that need resources. We firstly design a single-stage auction that determines the winning bids to satisfy the resource demands in polynomial time, while calculating the payments. Then, we design an online framework to tie a series of such single-stage auctions into a multi-stage online mechanism without requiring the knowledge of future bids and demands. Via rigorous analysis, we exhibit that our mechanism design achieves truthful bidding and individual rationality, with a constant competitive ratio regarding the social cost of the system in the long run. Finally, we verify the practical performance of our mechanism through extensive simulations.

We design a flexible data plane to integrate SDN and ENFV to manage complex network services for future mobile services. We build our prototype and show its feasibility in terms of latency and throughput by using mobile edge computing as an example use case in a realistic mobile networking testbed.

Edge computing paradigm aims at offloading microservices from mobile devices to the edge of network, reducing latency and increasing computational ability. Differently from the monolithic architecture, in a microservice-based system, an application is developed as a suite of microservices, each running independently on containers. To optimize the offloading decision, existing schemes often require a priori knowledge of the service type, latency-requirement, or both. Such limitations, in turn, hinders the quality-of-service (QoS) for real-time (mobile) applications. This paper presents FLAVOUR, a novel distributed and latency-optimal microservice scheduling mechanism for edge computing platform to achieve minimal service latency, while providing guaranteed transmission rates to minimize microservice completion times (MSCT). To design FLAVOUR, we first formulate a stochastic service delay minimization problem with constraints on MSCT and network stability. By solving this problem, we derive an optimal latency-optimal heuristic scheduling problem, which establishes the theoretical foundation for FLAVOUR. We have implemented a FLAVOUR prototype and evaluated FLAVOUR through both the testbed experiments and CloudSim simulations. Our preliminary results show that FLAVOUR holds great promise in terms of latency and throughput under different traffic dynamics.

Mobile edge computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; and 3) high quality-of-service requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services in order to achieve the optimized service latency and revenue. Furthermore, we consider the different priorities to prioritize the edge services for optimal service offloading. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue, and utilization.

Mobile Edge Computing (MEC) is emerging as one of the effective platforms for offloading the resource- and latency-constrained computational services of modern mobile applications. For latency- and resource-constrained mobile devices, the important issues include: 1) minimize end-to-end service latency; 2) minimize service completion time; 3) high quality-of-service (QoS) requirement to offload the complex computational services. To address the above issues, a latency-oblivious distributed task scheduling scheme is designed in this work to maximize the QoS performance and goodput for the MEC services. Unlike most of the existing works, we consider the latency-oblivious property of different services in order to achieve the optimized goodput and service latency. Furthermore, we design an optimal decision engine for efficiently offloading the computational services. Simulation results are presented to demonstrate the effectiveness of the proposed offloading scheme over other existing state-of-the-art solutions, in terms of service latency, goodput, service completion time and fairness.

Wireless body sensor networks (WBSNs), which efficiently and intelligently sense the physiological signals of the medical patients to support various medial applications, have allured tremendous attention from various research communities. For energy and resource constrained WBSNs, the important issues include: 1)~dynamic channel characteristics due to mobility and postural dynamics; 2) high energy efficiency owing to limited battery power; 3) high quality-of- service (QoS) requirement due to critical physiological data. To address the above issues, a cost-effective heuristic packet scheduling scheme is designed to provide the high network throughput and fair QoS to WBSNs. Unlike most of the existing works, we also consider the optimal delay- constraint in order to achieve the optimized packet transmission delay and to manage the heavy traffic load optimally. Specifically, we consider the critical factors of WBSNs to prioritize the data packets among access points, e.g., medical emergent patients have the higher priority to send their data packets than the normal patients. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed heuristic packet scheduling scheme over other existing state-of- the-art solutions, in terms of packet transmission delay, cost and network throughput.

In a post-disaster situation, increased concentration of patients in an area increases the traffic load of the network significantly, which degrades its performance with respect to mapping cost and network throughput. Therefore, to manage the increased traffic load and to provide ubiquitous medical services, we propose a disease-centric health-care management system using wireless body area networks (WBAN) in the presence of multiple health-cloud service providers (H-CSP). The theory of Social Network Analysis (SNA) is adopted to optimize the computational complexity and the traffic load of the network in an area, considering different disease types and the criticality indices of the WBANs. In such a scenario, Disease-centric Patient Group (DPG) formation among coexisting WBANs ensures optimized traffic load and reduced computational complexity. However, the formation of DPG alone is not sufficient to provide Quality-of-Service (QoS) to each WBAN. Therefore, to address these issues, we formulate a pricing model for the efficient mapping of critical WBANs from a DPG to a H-CSP to optimize the expected packet delivery delay and the network throughput. Consequently, to identify the critical WBANs from a DPG, we design a decision parameter based on an assortment of selection parameters. The performance of the Efficient Healthcare Management (HCM) scheme is analyzed based on distinct measures such as cost effectiveness, service delay, and throughput. Simulation results exhibit significant improvement in the network performance over the existing schemes.

Mobility induced by limb/body movements in Wireless Body Area Networks (WBANs) significantly affects the linkquality of intra-BAN and inter-BAN communication units, which, in turn, affects the Quality-of-Service (QoS) of each WBAN, in terms of reliability, efficient data transmission and network throughput guarantees. Further, the variation in link-quality between WBANs and Access Points (APs) makes the WBAN-equipped patients more resource-constrained in nature, which also increases the data dissemination delay. Therefore, to minimize the data dissemination delay of the network, WBANs send patients' physiological data to local servers using the proposed opportunistic transient connectivity establishment algorithm. Additionally, limb/body movements induce dynamic changes to the on-body network topology, which, in turn, increases the network management cost and decreases the life-time of the sensor nodes periodically. Also, mutual and cross technology interference among coexisting WBANs and other radio technologies increases the energy consumption rate of the sensor nodes and also the energy management cost. To address the problem of increased network management cost and data dissemination delay, we propose a network management cost minimization framework to optimize the network throughput and QoS of each WBAN. The proposed framework attempts to minimize the dynamic connectivity, interference management, and data dissemination costs for opportunistic WBAN. We have, theoretically, analyzed the performance of the proposed framework to provide reliable data transmission in opportunistic WBANs. Simulation results show significant improvement in the network performance compared to the existing solutions.

In a hospital environment, the total number of Wireless Body Area Network (WBAN) equipped patients requesting ubiquitous healthcare services in an area increases significantly. Therefore, increased traffic load and group-based mobility of WBANs degrades the performance of each WBAN significantly, concerning service delay and network throughput. In addition, the mobility of WBANs affects connectivity between a WBAN and an Access Point (AP) dynamically, which affects the variation in link quality significantly. To address the connectivity problem and provide Quality of Services (QoS) in the network, we propose a dynamic connectivity establishment and cooperative scheduling scheme, which minimizes the packet delivery delay and maximizes the network throughput. First, to secure the reliable connectivity among WBANs and APs dynamically, we formulate a selection parameter using a price-based approach. Thereafter, we formulate a utility function for the WBANs to offer QoS using a coalition game-theoretic approach. We study the performance of the proposed approach holistically, based on different network parameters. We also compare the performance of the proposed scheme with the existing state-of-the-art.

Wireless Body Area Networks (WBANs) are inherently resource-constrained in nature and each WBAN has different kind of Quality-of-Service (QoS) requirements. Therefore, in the presence of interference and poor link-quality, the resource pool of WBANs depletes significantly, which inherently increases the data dissemination delay and decreases the QoS requirements of WBANs in terms of resource availability. In order to minimize the data dissemination delay and to provide fair resources to WBANs in a link-failure situation, first we propose a fault tolerant mechanism for WBANs. Thereafter, we propose an energy-efficient resource management process to provide fair amount of resources to WBANs and minimize the energy consumption rate. We formulate the proposed scheme mathematically and evaluate through a series of simulations. Results show that the proposed scheme provides significant improvement in terms of delay, fairness and network throughput.

Increased population in an area degrades the performance of Wireless Body Area Networks (WBANs) in terms of throughput and packet delivery latency. WBANs by nature do not get fair amount of resources (bandwidth, time and spectrum). In this work, we consider the WBANs with varying traffic load in an area. In order to minimize the computational complexity of the algorithms executed the WBANs, the latter form different groups named Relational Patient Group (RPG), based on the disease types and the syndromes of the patients who are equipped with WBANs. RPG minimizes the computational complexity but does not minimize the traffic load. To minimize the traffic load the WBANs in the RPG form optimal grouping based on the optimal decision making process, named as Virtual Patient Group (VPG). We have formulated the proposed scheme mathematically and evaluated through a series of simulations. Results show that the proposed scheme provides significant improvement in the traffic load and the packet drop probability.