Chubo Liu

Strategy Configurations of Multiple Users Competition for Cloud Service Reservation

In this paper, we focus on strategy configurations of multiple users to make cloud service reservation. We consider the problem from a game theoretic perspective and formulate it into a non-cooperative game among the multiple cloud users, in which each user is informed with incomplete information of other users. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value. We solve the problem by employing variational inequality (VI) theory and prove that there exists a Nash equilibrium solution set for the formulated game. Then, we propose an iterative proximal algorithm (IPA), which is designed to compute a Nash equilibrium solution. The convergence of the IPA algorithm is also analyzed and we find that it converges to a Nash equilibrium if several conditions are satisfied. Finally, we conduct some numerical calculations to verify our theoretical analysis. The experimental results show that our proposed IPA algorithm converges to a stable state very quickly and improves the utilities of all users to certain extent by configuring a proper request strategy.

An approximation algorithm based on game theory for scheduling simple linear deteriorating jobs

Abstract We consider the scheduling of simple linear deteriorating jobs on parallel machines from a new perspective based on game theory. In scheduling, jobs are often controlled by independent and selfish agents, in which each agent tries to select a machine for processing that optimizes its own payoff while ignoring the others. We formalize this situation as a game in which the players are job owners, the strategies are machines, and a players utility is inversely proportional to the total completion time of the machine selected by the agent. The price of anarchy is the ratio between the worst-case equilibrium makespan and the optimal makespan. In this paper, we design a game theoretic approximation algorithm A and prove that it converges to a pure-strategy Nash equilibrium in a linear number of rounds. We also derive the upper bound on the price of anarchy of A and further show that the ratio obtained by A is tight. Finally, we analyze the time complexity of the proposed algorithm.

Slack allocation algorithm for energy minimization in cluster systems

Energy consumption has been a critical issue in high-performance computing systems, such as clusters and data centers. An existing technique to reduce energy consumption of applications is dynamic voltage/frequency scaling (DVFS). In this paper, we present a novel algorithm called EASLA for energy aware scheduling of precedence-constrained applications in the context of Service Level Agreement (SLA) on DVFS-enabled cluster systems. Due to the dependencies among tasks and makespan extension, there may be some underused slacks. The main idea of the EASLA algorithm is to distribute each slack to a set of tasks and scale frequencies down to try to minimize energy consumption. Specifically, it first finds the maximum set of independent tasks for each task, and then iteratively allocates each slack to the maximum independent set whose total energy reduction is the maximal. Randomly generated graphs and two real-world applications are tested in our experiments. The experimental results show that our scheduling algorithm can save up to 22.68% and 12.01% energy consumption compared with the GreedyDVS and EvenlyDVS algorithms respectively in homogeneous environments, and 12.33% energy consumption compared with the EES algorithm in heterogeneous environments. An energy-aware scheduling algorithm called EASLA is proposed.The main idea of the EASLA is to distribute slacks to tasks.The maximum set of independent tasks is involved.

A New Cloud Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users

In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.

SLA-based energy aware scheduling of precedence-constrained applications on DVFS-enabled clusters

The energy aware scheduling problem has been a critical issue in high-performance clusters owing to their high operation cost, environmental impact, and low reliability. An existing technique to reduce energy consumption of applications is dynamic voltage/frequency scaling (DVFS). In this paper, we develop an energy aware scheduling algorithm called EASLA for precedence-constrained applications in the context of Service Level Agreement (SLA) on DVFS-enabled cluster systems. Due to the dependencies among tasks and makespan extension, there may be some slacks under used. The main idea of the EASLA algorithm is to distribute each slack to a set of tasks and scale frequencies down to try to minimize energy consumption. Specifically, it first finds the maximum set of independent tasks for each task, and then iteratively allocates each slack to the maximum independent set whose total energy reduction is the maximal. Randomly generated graphs and two real-world applications are tested in our experiments. The experimental results show that our scheduling algorithm can save up to 22.68% and 12.01% energy consumption compared with GreedyDVS and EvenlyDVS algorithms, respectively.

Data-aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright

Bargaining Game-Based Scheduling for Performance Guarantees in Cloud Computing

In this article, we focus on request scheduling with performance guarantees of all users in cloud computing. Each cloud user submits requests with average response time requirement, and the cloud provider tries to find a scheduling scheme, i.e., allocating user requests to limited servers, such that the average response times of all cloud users can be guaranteed. We formulate the considered scenario into a cooperative game among multiple users and try to find a Nash bargaining solution (NBS), which can simultaneously satisfy all users’ performance demands. We first prove the existence of NBS and then analyze its computation. Specifically, for the situation when all allocating substreams are strictly positive, we propose a computational algorithm (CA), which can find the NBS very efficiently. For the more general case, we propose an iterative algorithm (IA), which is based on duality theory. The convergence of our proposed IA algorithm is also analyzed. Finally, we conduct some numerical calculations. The experimental results show that our IA algorithm can find an appropriate scheduling strategy and converges to a stable state very quickly.