Abstract
This research targets workload partitioning for GPUs, TPUs and CPUs in heterogeneous distributed systems. This paper focuses on the issues connected with the heterogeneity of the hardware, balanced loads, latency of data transfer and energy consumption. It highlights the characteristics of proactive scheduling algorithms for resources, which enhance the system’s efficiency and capability. These applications are selected in the areas of machine learning and artificial intelligence, big data and cloud computing and scientific computing and modelling to demonstrate how systems with heterogeneity can improve certain computational workloads. Finally, the study provides information on fault tolerance and cost-effectiveness as a way of finding efficient and effective ways of managing available resources for mixed-distributed environments.

Keywords: Resource management, Heterogeneous systems, Load balancing, GPU, TPU, CPU, Distributed computing, Performance optimization, Scalability, Task scheduling

1. Introduction
The efficient management of computational resources in heterogeneous distributed systems is critical in optimizing performance and minimizing operational costs. The systems consist of these heterogeneous processing units-Graphics Processing Units (GPUs), Tensor Processing Units (TPUs) and Central Processing Units (CPUs), which perform particularly well at specialized computation tasks. GPUs are designed for parallel workloads, like deep learning and scientific simulations, where things are parallel; TPUs are optimized for neural network inference and training; CPUs offer versatility, good for things that aren’t parallel or have branching (instructions that cause them to go to a different path). These heterogeneous units must be scheduled, resources must be allocated and loads must be balanced so that they do not incur bottlenecks and so that the resources are used optimally. In this research, we present an analysis of the interactions between hardware architecture, workload characteristics and dynamic scheduling mechanisms. The technical frameworks discussed include containerized environments and resource orchestration, workload profiling and runtime optimization strategies. This research takes an important step in the direction of improving the productivity of these mixed-distributed systems by providing solutions for such issues as contention, thermal management and power consumption.

1.1. Resource Scheduling in Heterogeneous Systems
Workload distribution between computing units of different types, GPUs, TPUs and CPUs, requires resource scheduling in heterogeneous distributed systems. Traditional resource management approaches are designed to work for homogeneous systems and although capable, they do not fully exploit dedicated hardware, such as GPUs (graphics processing units) and TPUs (tensor processing units)¹. As heterogeneous systems arise, we must develop algorithms for dynamically placing tasks across diverse processing units sensitive to item-level workload, available processing power and user-specified energy utilization.

A few recently published papers at the Reinforcement Learning and Deep Discrete Optimization Workshop showed how recent scheduling algorithm advances can be used to predict where workloads should be placed to optimize performance metrics based on historical data using deep learning techniques. Furthermore, the problem's inherent complexity is addressed through scheduling in heterogeneous environments using techniques like Reinforcement Learning (RL) and Multi Objectives Optimization (MOO). These methods aim to optimize power efficiency while minimizing response time with respect to throughput balancing². In environments of distributed workload, it is necessary to avoid overload of particular processing units or to use impactfully resources by local and functional schedulers in a system. Thus, in addition, container orchestration systems like Kubernetes have been extended to support heterogeneous hardware by integrating native device drivers for GPUs and TPUs to provide dynamic resource allocation on the basis of task demand.

1.2. Workload partitioning and distribution strategies
Workload partitioning and distribution strategies that are efficient for a distributed environment, in which workloads and hardware accelerators are distributed, are important for balancing multiple modes in diverse hardware accelerators. In systems with hardware heterogeneity, tasks also need to be decomposed in a way that best takes advantage of the performance of each hardware unit (CPUs generally are better for doing low parallel tasks, GPUs and TPUs are better at highly parallel tasks like matrix multiplications or training deep learning models).

Task cloning was studied in³ as one method of automatic workload partitioning: complex tasks are split into several subtasks and these are assigned to the best hardware unit in terms of their computational capacity. In recent years, hybrid parallelism, having separate execution streams within a GPU or CPU, has been used to improve performance for batched operations in deep learning models ranging through frameworks such as TensorFlow and PyTorch. To implement these systems, we rely on both model parallelism and data parallelism strategies for efficient splitting of computational workloads. Also, we have a workload distribution scheme that includes load balancing and moving tasks between devices in order to avoid bottlenecks and devices that get utilized to maximum potential without under-utilisation or over-utilisation.

Different models implement predictive analytics to forecast workloads and preemptively allocate resources in order to increase the accuracy of task scheduling. Further, in making the allocation of resources to tasks more intelligent and adaptive, machine learning-based methods are employed to optimize task mapping as a function of historical performance data. To minimize all kinds of hardware conflicts while maximizing total system performance, the challenge lies in balancing heterogeneous workloads.

1.3. Energy efficiency and power-aware resource management
One central concern in heterogeneous distributed systems is energy consumption because a dedicated hardware accelerator requires a high amount of power. The goal of power-aware resource management is to minimize energy consumption that is subject to a certain level of system performance and independence from energy grids. Hardware-level power measurement tools are utilized in these systems to monitor and dynamically adjust the use of the resource based on workload demands.

Power-aware scheduling has been proposed by a variety of techniques, such as dynamic voltage and frequency scaling (DVFS) and energy-efficient job placement algorithms. DVFS lowers the frequency used by the processing units to fit the workload and vice versa. The goal of energy-aware job placement is to place tasks into processing units that have capabilities to provide good performance and energy efficiency for the device. In addition to such means, there are some applications that incorporate machine learning models that predict the workloads’ energy consumption and, consequently, perform scheduling optimization.

1.4. Fault tolerance and reliability in heterogeneous systems
In this environment, system dependability is more significant because failure characteristics and rates of heterogeneous computing platforms often differ - the heterogeneous array may contain, for instance, GPUs, TPUs and CPUs. In any of these processing units, some failures may cause a system to become unstable and may also lead to inefficiency. They have tackled these challenges by implementing other fault-tolerant mechanisms of the resource management system, which work in real-time to discover and rectify the faults.

Such methods as redundancy and checkpointing are more familiar when used in fault-tolerance systems. Redundancy is where instead of keeping one device that does a certain job, two or more such machines are employed; in case one fails, the other is there to take over without anyone noticing. Checkpointing saves the state periodically and so in the event of failure, it becomes easier to restore the system to the most recent previous stable state⁴. Furthermore, passive monitoring is commonly used to predict hardware failures before they occur since possible actions are to transfer resources to other locations in an attempt to avoid potential breakdowns.

Other mechanisms, including Paxos and Raft for the distributed consensus, are further discussed to maintain reliability in heterogeneous systems. These protocols aid in keeping data duplicated and synchronized so that computations can go on even with some nodes out of reach. Managed effectively, the inclusion of fault tolerance within the resource management strategy means that the system stays resilient in the face of hardware dysfunction, including high availability with minimal outages.

1.5. Scalability and adaptation to dynamic environments
In heterogeneous distributed systems, where the resource like CPU, GPU or even TPU is being dynamically allocated to cope with fluctuating workload as well as system context, scalability is a major challenge⁵. And these systems must scale horizontally, adding or removing processing units based on the workload.

Such elastic scaling solutions as Kubernetes and Apache Mesos have gained a lot of attention by offering a kind of dynamism, especially in the use of heterogeneous facilities to scale up or down as needed. They adapt the resources used by the system where the workload is required according to the processing requirements needed for the workload. However, achieving efficient scaling between different approximate hardware types poses definite challenges. Workload distribution across GPUs, TPUs and CPUs is a complex process: the system has to take into account not only the number of processing units but also their performance, energy consumption and other factors⁶.

Further complicating the adaptation is the fact that we are operating in dynamic environments, with additional external factors such as varying network conditions, node availability and unpredictable user workloads. This research has resulted in the development of adaptive resource allocation algorithms capable of dynamically adjusting resource distribution for both optimized performance and lowered cost (Figure 1).



Figure 1: Resource Isolation and Virtualization.

Using resource isolation and virtualization, this diagram depicts efficient task execution without conflicts between workloads of different hardware types.

In the following, we describe an informative table summarizing the key resource management strategies that enable balancing GPU, TPU and CPU workloads in heterogeneous distributed systems⁷. The table illustrates some techniques, their use and the challenges that these techniques address.

The current table clearly explains in detail the resource management strategies in heterogeneous distributed systems along with the challenge of workload balancing of GPUs, TPUs and CPUs, fault tolerance, scalability and load balancing. Each strategy includes techniques that optimize performance and address system complexities. The essential property of robust scaling strategies and dynamic adaptation techniques can be used to satisfy the requirements of modern computing workloads for heterogeneous distributed systems, which provide the necessary flexibility and efficiency in the manner of using resources in the many processing units available.