Deep Reinforcement Learning-Based Workload Scheduling for Edge Computing

Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of users, heterogeneous applications and intermittent traffic. In such environment, edge computing often suffers from unbalance resource allocation, which leads to task failure and affects system performance. To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and the failed task rate. Meanwhile, We adopt Deep-Q-Network(DQN) algorithms to solve the complexity and high dimension of workload scheduling problem. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine(VM) utilization, and failed tasks rate compared with other approaches. Our DRL-based approach can provide an efficient solution to the workload scheduling problem in edge computing.

A trusted de-swinging k-anonymity scheme for location privacy protection

K-anonymity has been gaining widespread attention as one of the most widely used technologies to protect location privacy. Nevertheless, there are still some threats such as behavior deception and service swing, since utilizing distributed k-anonymity technology to construct an anonymous domain. More specifically, the coordinate of the honest node will be a leak if the malicious nodes submit wrong locations coordinate to take part in the domain construction process. Worse still, owing to service swing, the attacker increases the reputation illegally to deceive honest nodes again. To overcome those drawbacks, we propose a trusted de-swinging k-anonymity scheme for location privacy protection. Primarily, we introduce a de-swinging reputation evaluation method (DREM), which designs a penalty factor to curb swinging behavior. This method calculates the reputation from entity honesty degree, location information entropy, and service swing degree. Besides, based on our proposed DREM, a credible cloaking area is constructed to protect the location privacy of the requester. In the area, nodes can choose some nodes with a high reputation for completing the construction process of the anonymous domain. Finally, we design reputation contracts to calculate credit automatically based on smart contracts. The security analysis and simulation results indicate that our proposed scheme effectively resists malicious attacks, curbs the service swing, and encourages nodes to participate honestly in the construction of cloaking areas.

Scalability resilience framework using application-level fault injection for cloud-based software services

This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the faults at the application level affect the scalability resilience and behaviour of cloud-based software services. Previous studies on scalability analysis of cloud-based software services provide a baseline of the scalability behaviour of such services, allowing to conduct in-depth scalability investigation of these services. Experimental analysis on the EC2 cloud using a real-world cloud-based software service is used to demonstrate the framework, considering delay latency of software faults with two varied settings and two demand scenarios. The experimental approach is explained in detail. Here we simulate delay latency injection with two different times, 800 and 1600 ms, and compare the results with the baseline data. The results show that the proposed approach allows a fair assessment of the fault scenario’s impact on the cloud software service’s scalability resilience. We explain the use of the methodology to determine the impact of injected faults on the scalability behaviour and resilience of cloud-based software services.

Security-driven distributed platforms for intellectual property resource provision - a case study of TSITE IP

In the information age, with the vigorous development of big data and artificial intelligence, intellectual property protection is an essential part of the current scientific and technological development. Intellectual property related data grows in a geometric progression, so the demand for IP data storage space is also increasing day by day. With the rise of cloud computing technology, intellectual property data distributed platforms based on cloud storage have also been produced one after another. Because the biggest feature of cloud storage is that storage is a service, it puts forward higher requirements for the intellectual property service industry. Firstly, it introduces the domestic intellectual property cloud platform services from the perspectives of government support, state-owned enterprises and private enterprises; Secondly, four typical distributed platforms provided by commercial resources are selected to introduce their operation modes, focusing on the problems faced by domestic intellectual property service modes; Secondly, it compares and discusses the current situation of domestic intellectual property distributed platforms; Then, aiming at the current domestic intellectual property service mode, taking tsite as an example, this paper puts forward the design and construction strategy of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

Pricing strategies of an oligopolist in federated cloud markets

In this paper, we study how an oligopolist influences the coalition structure in federated cloud markets. Specifically, we use cooperative game theory to model the circumstances under which a cloud provider prefers to join a cloud federation vis-a-vis consider taking a price offer made by an oligopolist. We consider two price offering strategies for an oligopolist: non-adaptive and adaptive. In non-adaptive strategy, an oligopolist makes a price offer to all the cloud providers simultaneously. It can be noted that the oligopolist can buy-out all the cloud providers by making a price offer which is equal to a core allocation and the total price offer made by the oligopolist is equal to the value of the grand coalition. In adaptive strategy, the oligopolist approaches the cloud providers one after another in a sequential manner. We show that by using the adaptive strategy, the oligopolist can buy-out all the cloud providers at a total price offer which is less than that of the non-adaptive strategy.

An efficient population-based multi-objective task scheduling approach in fog computing systems

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

A blockchain index structure based on subchain query

Blockchain technology has the characteristics of decentralization and tamper resistance, which can store data safely and reduce the cost of trust effectively. However, the existing blockchain system has weak performance in data management, and only supports traversal queries with transaction hashes as keywords. The query method based on the account transaction trace chain (ATTC) improves the query efficiency of historical transactions of the account. However, the efficiency of querying accounts with longer transaction chains has not been effectively improved. Given the inefficiency and single method of the ATTC index in the query, we propose a subchain-based account transaction chain (SCATC) index structure. First, the account transaction chain is divided into subchains, and the last block of each subchain is connected by a hash pointer. The block-by-block query mode in ATTC is converted to the subchain-by-subchain query mode, which shortens the query path. Multiple transactions of the same account in the same block are merged and stored, which simplifies the construction cost of the index and saves storage resources. then, the construction algorithm and query algorithm is given for the SCATC index structure. Simulation analysis shows that the SCATC index structure significantly improves query efficiency.

A rational delegating computation protocol based on reputation and smart contract

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Next, to satisfy the fairness of the rational delegating computation, we propose a rational delegating computation protocol based on reputation and smart contract. More specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. The reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Then, we conduct a comprehensive experiment to evaluate the proposed protocol. The simulation and analysis results show that the proposed protocol solves the complex traditional verification problem. We also conduct a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network. Meanwhile, we prove the fairness and correctness of the protocol.