In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (MultiObjective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Summary Based on a pay‐as‐you‐go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy‐aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real‐time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real‐time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy‐aware resource provisioning mechanism for cloud data centers, which are capable of serving real‐time periodic tasks following the Software as a Service model. The proposed method is compared against an energy‐aware version of the RT‐OpenStack. RT‐OpenStack is a recently proposed approach to provide a time‐predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy‐aware version of the RT‐OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large‐scale data centers is more considerable.

Summary Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time‐critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software‐defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run‐time. It is of paramount importance to select a proper number of these devices (i.e., software‐defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time‐critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA‐MSCP, an algorithm hybridizing a parallel version of the max‐min ant system with simulated annealing for multiple‐sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well‐known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.

Cloud computing is receiving an increasing attention when it comes to providing a wide range of cost-effective services. In this context, energy consumption of communication and computing resources contribute to a major portion of the cost of services. On the other hand, growing energy consumption not only results in a higher operational cost, but it also causes negative environmental impacts. A large number of cloud applications in, e.g., telecommunication, multimedia, and video gaming, have real-time requirements. A cloud computing system hosting such applications, that requires a strict timing guarantee for its provided services, is denoted a Real-Time Cloud (RTC). Minimizing energy consumption in a RTC is a complicated task as common methods that are used for decreasing energy consumption can potentially lead to timing violations. In this paper, we present an online energy-aware resource provisioning framework to reduce the deadline miss ratio for real-time cloud services. The proposed provisioning framework not only considers the energy consumption of servers but it also takes the energy consumption of the communication network into account, to provide a holistic solution. An extensive range of simulation results, based on real data, show a noticeable improvement regarding energy consumption while keeping the number of timing violations less than 1% in average.

An AUTOSAR-based software application contains a set of software components, each of which encapsulates a set of runnable entities. In fact, the mission of the system is fulfilled as result of the collaboration between the runnables. Several trends have recently emerged to utilize multi-core technology to run AUTOSAR-based software. Not only the overhead of communication between the runnables is one of the major performance bottlenecks in multi-core processors but it is also the main source of unpredictability in the system. Appropriate mapping of the runnables onto a set of tasks (called mapping process) along with proper allocation of the tasks to processing cores (called task allocation process) can significantly reduce the communication overhead. In this paper, three solutions are suggested, each of which comprises both the mapping and the allocation processes. The goal is to maximize key performance aspects by reducing the overall inter-runnable communication time besides satisfying given timing and precedence constraints. A large number of randomly generated experiments are carried out to demonstrate the efficiency of the proposed solutions.

Distributed system has been developed as a platform for huge computations. Reliability is one of the prominent issues in such systems. Many studies have been recently done to improve reliability by proper task allocation in distributed systems, but they have only considered some system constraints such as processing load, memory capacity, and communication rate. In this paper, we consider time constraint in form of task deadline to above-mentioned constraints in order to model and analyze reliability in distributed real-time systems. To maximize reliability besides satisfying the constraints, we proposed a new offline task allocation algorithm. The algorithm is Systematic Memory-based Simulated Annealing (SMSA) which uses a monotonic cooling schedule and limited memory to store recently visited solutions to prevent cycling. In addition, an effective greedy heuristic algorithm intensifies SMSA. For evaluating the algorithm, SMSA is compared with Genetic Algorithm (GA) and Simulated Annealing (SA). Results have shown that in contrast to SA and GA, SMSA obtains satisfactory reliability in reasonable execution time. Meanwhile, SMSA meets all deadlines same as SA and GA. Furthermore, SMSA results have low deviation from average reliability.

Cloud computing is widely referred as the next generation of computing systems. Reliability is a key metric for assessing performance in such systems. Redundancy and diversity are prevalent approaches to enhance reliability in Cloud Computing Systems (CCS). Proper resource allocation is an alternative approach to reliability improvement in such systems. In contrast to redundancy, appropriate resource allocation can improve system reliability without imposing extra cost. On the other hand, contemplating reliability irrespective of Quality of Service (QoS) requirements may be undesirable in most of CCSs. In this paper, we focus on resource allocation approach and introduce an analytical model in order to analyze system reliability besides considering application and resource constraints. Task precedence structure and QoS are taken into account as the application constraints. Memory and storage limitation of each server as well as maximum communication load on each link are considered as the principle resource constraints. In addition, effect of network topology on system reliability is discussed in detail and the model is extended to cover various network topologies.

Multi-core technology is recognized as a key component to develop new cost-efficient products. It can lead to reduction of the overall hardware cost through hardware consolidation. However, it also results in tremendous challenges related to the combination of predictability and performance. The AUTOSAR consortium has developed as the worldwide standard for automotive embedded software systems. One of the prominent aspects of this consortium is to support multi-core systems. In this paper, the ongoing work on addressing the challenge of achieving a resource efficient and predictable mapping of AUTOSAR runnables onto a multi-core system is discussed. The goal is to minimize the runnables' communication cost besides meeting timing and precedence constraints of the runnables. The basic notion utilized in this research is to consider runnable granularity, which leads to an increased flexibility in allocating runnables to various cores, compared of task granularity in which all of the runnables hosted on a task should be allocated on the same core. This increased flexibility can potentially enhance communication cost. In addition, a heuristic algorithm is introduced to create a task set according to the mapping of runnables on the cores. In our current work, we are formulating the problem as an Integer Linear Programming (ILP). Therefore, conventional ILP solvers can be easily applied to derive a solution.