ABSTRACT In dynamic and unpredictable environments, opportunistic edge computing has emerged as a novel paradigm. It aims to leverage temporarily available computational resources to enable efficient data processing. To tackle the challenges of mobile group collaborative data aggregation in such contexts, this paper proposes a Multi‐Preference Redundant Data Collaborative Aggregation algorithm (MPRDCA), which jointly optimizes channel state and data preference weighting, and pioneers three key innovations: (a) a novel binary decision factor integrating dynamic user preferences with channel states, (b) a time‐varying utility function incorporating preference weights, and (c) Lyapunov‐based energy constraint transformation. Performance was rigorously evaluated via parameterized stochastic simulations incorporating probabilistic channel states and hierarchical preference coefficients, modeling a dynamic edge group. Key results demonstrate that under stringent energy constraints, MPRDCA achieves higher overall transmission utility while enhancing effective transmission volume of the most urgently required data by up to 41.99% compared to benchmarks. Theoretical and empirical results validate MPRDCA as a highly adaptive solution for resource‐constrained edge scenarios, achieving optimal utility‐efficiency trade‐offs.

Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of a single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fa u lt-Tolera n t Cooperat i ve C o mputi n g) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is 𝒪(|ℐ| 2 +|𝒩|) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies.

Efficiently managing database instances over cloud-scale clusters is significant for increasing service quality and reducing operational cost, especially confronting the growing cluster size and heterogeneous application services. Alibaba Cloud provides a large-scale Relational Database Service (RDS) for millions of users including enterprises from start-ups to large international corporations. To manage tremendous amount of RDS instances in the Cloud with the goal of reducing cost while guaranteeing service level agreement(SLA), YISHAN, an intelligent database instance management system, is designed to dynamically manage the placement of instances using machine learning techniques. YISHAN collects historical performance data to analyze patterns of the resource utilization of instances and hosts. By learning “good packings” in which instances colocate harmoniously, YISHAN is able to optimize the instances placement to provide better quality of service and improve the efficiency of CPU, memory, and disk resources. We deploy and run YISHAN in Alibaba Cloud RDS. The running logs show that YISHAN successfully saves 17% of the resources in hosts and efficiently reduces the burdens and crash risks of RDS instances.

As accessing remote cloud via cellular network is costly due to the lower bandwidth, higher wide area network (WAN) latency, and higher energy consumption, mobile cloudlet that formed by several edge mobile devices has become an emerging computing paradigm and attracted increasing attention recently. Being different from the existing studies that mainly focus on the issue of computation offloading among the peers, this paper investigates the problem of cooperative data sharing among peers to overcome the data dissymmetry, especially with the presence of dynamic network context. First, a publish/subscribe-based data sharing model is designed to cope with the unpredictable communication condition. Then, the data transmission scheduling within cooperative mobile devices is formulated as a utility maximization optimization considering the limited channel capacity, heterogeneous quality of experience (QoE) requirements, and incentive mechanism for participation. To encourage cooperation among mobile devices, a data downloading/uploading queuing mechanism is elegantly designed. Furthermore, an online algorithm without predicting the future information on request arrivals and network changes is developed to simultaneously optimize data transmission and communication interface selection in the long run. Theoretical analysis shows that the proposed algorithm is able to obtain a utility arbitrarily close to the offline optimum and guarantee the delay bound. Simulations demonstrate the effectiveness and the superiority of the proposed algorithm over some existing typical strategies.

With the globalization of service, organizations continuously produce large volumes of data that need to be analysed over geo-dispersed locations. Traditionally central approach that moving all data to a single cluster is inefficient or infeasible due to the limitations such as the scarcity of wide-area bandwidth and the low latency requirement of data processing. Processing big data across geo-distributed datacenters continues to gain popularity in recent years. However, managing distributed MapReduce computations across geo-distributed datacenters poses a number of technical challenges: how to allocate data among a selection of geo-distributed datacenters to reduce the communication cost, how to determine the Virtual Machine (VM) provisioning strategy that offers high performance and low cost, and what criteria should be used to select a datacenter as the final reducer for big data analytics jobs. In this paper, these challenges is addressed by balancing bandwidth cost, storage cost, computing cost, migration cost, and latency cost, between the two MapReduce phases across datacenters. We formulate this complex cost optimization problem for data movement, resource provisioning and reducer selection into a joint stochastic integer nonlinear optimization problem by minimizing the five cost factors simultaneously. The Lyapunov framework is integrated into our study and an efficient online algorithm that is able to minimize the long-term time-averaged operation cost is further designed. Theoretical analysis shows that our online algorithm can provide a near optimum solution with a provable gap and can guarantee that the data processing can be completed within pre-defined bounded delays. Experiments on WorldCup98 web site trace validate the theoretical analysis results and demonstrate that our approach is close to the offline-optimum performance and superior to some representative approaches.

Due to the limitation of resources, preemption frequently occurs in almost all the commercial cloud platforms, such as Google cluster and Amazon cluster. Since preemption can ensure that once the system is in heavy workload, high-priority tasks will be executed primarily and at the same time, some low-priority tasks will be killed immediately. Then when more resources are available, the killed tasks will restart to execute. Especially, during the peak time, some low-priority tasks could possibly be preempted and restarted repeatedly resulting in much more consuming precious resources including CPU cores, RAM and hard drives. Thanks to the checkpoint technology, it provides an efficient solution to addressing the preemption issue. But checkpoint technology has limitations, e.g., making checkpoint frequently will add redundant overhead to the cluster and cause I/O congestion. In this paper, by leveraging checkpoint technology, we designed a novel approach to improving the performance of shared clusters. Specifically, by checking the occupancy of resources periodically, making decisions to checkpoint or not and checkpointing for certain tasks, our method can reduce unnecessary checkpoints and exalt the performance of the whole cloud, especially tasks with low-priority. Extensive simulation experiments injecting tasks following the Google cloud trace logs were conducted to validate the superiority of our approach by comparing it with some baselines.

Mobile cloud computing has become an emerging computing paradigm to extend the capability of the mobile devices and it has gained increasing popularity in recent years. Existing studies mainly focus on how to leverage the computing capability of the individual device by employing the capability from remote cloud datacenters or local mobile cloud formed by nearby devices. Different from these studies, we investigate how to improve the performance of data sharing in the peer-to-peer mobile cloud, with the limited bandwidth and the presence of dynamic and unpredictable wireless channel state. Specifically, we first formulate the data transmission among devices as a utility maximization problem with the consideration of limited bandwidth, incentive participation and the QoE (Quality of Experience) heterogeneity, based on incorporating publish/subscribe component into the base station. Then, a dynamic online algorithm, which does not need the future context (e.g., channel state) of the mobile cloud, is developed to simultaneously make the decision of data transmission and communication interface selection. Rigorously theoretical analysis shows the optimality and the effectiveness of the proposed algorithm. Extensive experiments are conducted to verify the analysis results and the superiority of the proposed algorithm over existing strategies.

Cloud computing provides a new opportunity for Video Service Providers (VSP) to running compute-intensive video applications in a cost effective manner. Under this paradigm, a VSP may rent virtual machines (VMs) from multiple geo-distributed datacenters that are close to video requestors to run their services. As user demands are difficult to predict and the prices of the VMs vary in different time and region, optimizing the number of VMs of each type rented from datacenters located in different regions in a given time frame becomes essential to achieve cost effectiveness for VSPs. Meanwhile, it is equally important to guarantee users' Quality of Experience (QoE) with rented VMs. In this paper, we give a systematic method called Dynamical Request Redirection and Resource Provisioning (DYRECEIVE) to address this problem. We formulate the problem as a stochastic optimization problem and design a Lyapunov optimization framework based online algorithm to solve it. Our method is able to minimize the long-term time average cost of renting cloud resources while maintaining the user QoE. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing. Extensive experiments shows that our method is adaptive to request pattern changes along time and outperforms existing algorithms.

Dense sample video patches have been used for video representation in action recognition and achieve better performance than sparse spatiotemporal local features. However, two problems of this method must be considered. First one, many video patches are from background other than human body. Second one, the descriptor is not reliable, since it is neither shift nor scale invariant. To solve these two problems, we proposed an Optimized Video Dense Sampling (OVDS) method combing with dense sampling and spatiotemporal interest points detector. OVDS densely sampled video patches with optimizing the position and scale parameters to guarantee the features are shift and scale invariant. To omit the action unrelated features, we extracted video patches only from human body regions instead of the whole videos. Experimental results on KTH, Weizmann, UCF, Hoollywood2 datasets showed that the features detected by OVDS are informative and reliable for action recognition, and achieve better performance over the existing spatiotemporal local features.

Recently space-time interest points (STIPs) using bag-of-feature (BOF) in action recognition has been highly successful. Despite its popularity, The quantization error and the lost of semantic meaning among STIPs are the main weaknesses that severely limit the effectiveness of this method. To overcome these limitations, this paper incorporated the feature position information into coding procedure and proposed a novel Feature Position Constrained Linear Coding (FPLC) method by extending the Locality Constrained Linear Coding (LLC) approach. It first project the features into the human ROI, then codes the features locally using FPLC with the consideration of feature position. Owning to that the local area of human ROI often aggregate features extracted from the same part of human body and those features should exhibit similar values, this local coding strategy helps to alleviate the quantization error and enhance correlation between features at the same time, which helps to improve the recognition accuracy. Compared with the state-of-the-art action recognition method, experiment results demonstrated the effectiveness of the proposed method.