ABSTRACT Designing adaptive and energy‐aware scheduling algorithms for scientific workflows has become increasingly important as high‐performance computing (HPC) systems evolve toward large‐scale heterogeneous architectures. This paper proposes PPO–KAN, a hybrid workflow scheduling framework that integrates proximal policy optimization (PPO) with Kolmogorov–Arnold Networks (KAN) to jointly optimize makespan and energy consumption. PPO enables adaptive policy learning in dynamic resource environments, while the KAN‐based policy representation enhances expressiveness by modeling complex nonlinear task–resource relationships. The proposed framework supports three reward formulations—makespan‐oriented, energy‐oriented, and weighted multi‐objective—allowing explicit control over optimization trade‐offs. Extensive experiments conducted on four benchmark scientific workflows—Epigenomics (904 tasks), LIGO (922 tasks), Montage (902 tasks), and SIPHT (1004 tasks)—using simulated heterogeneous HPC clusters demonstrate that PPO–KAN achieves up to a 41.7% reduction in makespan and a 28.3% reduction in energy consumption compared with NSGA‐II and PPO with feedforward neural networks. Furthermore, convergence analysis shows faster and more stable learning, with policies stabilizing within 50–100 episodes. Ablation studies further confirm that architectural enhancements improve energy efficiency and policy robustness. Overall, the results indicate that PPO–KAN provides an effective and scalable solution for multi‐objective workflow scheduling in heterogeneous HPC environments.

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks.

Fog computing platforms became essential for deploying low-latency applications at the network's edge. However, placing and managing time-critical applications over a Fog infrastructure with many heterogeneous and resource-constrained devices over a dynamic network is challenging. This paper proposes an incremental multilayer resource-aware partitioning (M-RAP) method that minimizes resource wastage and maximizes service placement and deadline satisfaction in a dynamic Fog with many application requests. M-RAP represents the heterogeneous Fog resources as a multilayer graph, partitions it based on the network structure and resource types, and constantly updates it upon dynamic changes in the underlying Fog infrastructure. Finally, it identifies the device partitions for placing the application services according to their resource requirements, which must overlap in the same low-latency network partition. We evaluated M-RAP through extensive simulation and two applications executed on a real testbed. The results show that M-RAP can place 1.6 times as many services, satisfy deadlines for 43% more applications, lower their response time by up to 58%, and reduce resource wastage by up to 54% compared to three state-of-the-art methods.

In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing the workload traces of these clusters has been an arduous task due to the heterogeneous nature of data. This article proposes a scalable infrastructure based on Google's dataproc for analyzing the workload traces of cloud environments. We evaluated the functioning of the proposed infrastructure using the workload traces of Google cloud cluster-usage-traces-v3. We perform the workload characterization on this dataset, focusing on the heterogeneity of the workload, the variations in job durations, aspects of resources consumption, and the overall availability of resources provided by the cluster. The findings reported in the paper will be beneficial for cloud infrastructure providers and users while managing the cloud computing resources, especially serverless platforms.

Function-as-a-Service (FaaS) is an attractive cloud computing model that simplifies application development and deployment. However, current serverless compute platforms do not consider data placement when scheduling functions. With the growing demand for edge-cloud continuum, multi-cloud, and multi-serverless applications, this flaw means serverless technologies are still ill-suited to latency-sensitive operations like media streaming. This work proposes a solution by presenting a tool called FaDO: FaaS Functions and Data Orchestrator, designed to allow data-aware functions scheduling across multi-serverless compute clusters present at different locations, such as at the edge and in the cloud. FaDO works through header-based HTTP reverse proxying and uses three load-balancing algorithms: 1) The Least Connections, 2) Round Robin, and 3) Random for load balancing the invocations of the function across the suitable serverless compute clusters based on the set storage policies. FaDO further provides users with an abstraction of the serverless compute cluster’s storage, allowing users to interact with data across different storage services through a unified interface. In addition, users can configure automatic and policy-aware granular data replications, causing FaDO to spread data across the clusters while respecting location constraints. Load testing results show that it is capable of load balancing high-throughput workloads, placing functions near their data without contributing any significant performance overhead.

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

Virtual machine image retrieval a b s t r a c tInfrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale.The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning.Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads.In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads.To achieve this goal, Expelliarmus incorporates three complementary features.First, it models VMIs as semantic graphs to facilitate their similarity computation.Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages.Third, it assembles VMIs based on the required software packages upon user request.We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed.Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3 -22 times compared to state-of-the-art systems (e.g.IBM's Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.

IoT-enabled applications, such as cloud manufacturing, guided water quality or air quality analysis, energy-conscious societal applications, and smart agricultural economics, are designed using a blend of high-end computing technologies, such as cloud, edge, and fog. Smart cities and governmental authorities keep a keen eye out for implementing IoT applications in an automated/decentralized approach with enhanced security measures so that tens of thousands of users, including entrepreneurs, are benefited. Existing IoT architectures are prone to energy inefficiency or resource underutilization problems due to the avoidance of apt technologies, such as serverless computing. This article proposes to set forth a serverless blockchain-enabled IoT architecture for societal applications. It explores the existing IoT architectures and pinpoints the advantages of applying serverless blockchains on IoT architectures. In addition, the proposed IoT architecture is illustrated with a specific use case of IoT societal applications namely air quality monitoring for smart cities (AQMS). This article discloses how air quality sensor data from defective industries were securely transacted to blockchain networks surpassing from the three levels of computing namely edge, fog, and cloud while utilizing serverless and server-oriented functions. In addition, this article exposes a list of the most potent serverless functions that assist AQMS IoT societal applications in detail. The IoT architecture, discussed in this article, will enable innovations and research works for IoT developers and researchers.

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator. The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.